Bibliographic record
Abstract
This article examines a case of recommendation implementation in evaluation practice. It summarizes the results of a performance audit, Management of Programs for First Nations, completed by the Office of the Auditor General of Canada and reported to the Canadian Parliament in May 2006. The performance audit took an innovative approach to examining First Nations programs that included applying a causal lens to identify and understand factors critical to the successful use of recommendations in complex government decision-making. The performance audit assessed the progress of federal departments in implementing recommendations that the Auditor General had made in audits reported between 2000 and 2003 on First Nations issues. The Office of the Auditor General's performance audits usually make recommendations and sometimes follow-up audits report on their implementation, but typically do not address the reasons behind the progress of adoption; this audit was different in that it attempted to ascertain some of the reasons for progress or the lack of progress. This innovative approach resulted in the identification of several factors that appear to be critical to the successful implementation of recommendations, and to the successful design and delivery of programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".